Image edge processing method, electronic device, and computer readable storage medium

ABSTRACT

An image edge processing method is disclosed. The method includes steps of: extracting a brightness component from an input image; calculating an edge probability value mp of each pixel in the image according to the extracted brightness component; calculating an enhancement coefficient A for each pixel based on the edge probability value mp; performing a noise detection according to the brightness component, and determining if each pixel in the image is a noise point; when the pixel is not a noise point, performing a logarithmic processing to the pixel in order to obtain a data w; enhancing an edge of the image according to the A, the w and the brightness component in order to obtain an enhanced brightness component data; and after performing a brightness component synthesis according to the enhanced brightness component data, outputting an enhanced image. An electronic device and computer readable storage medium are also disclosed.

FIELD OF THE INVENTION

The present invention relates to an image edge processing method; anelectronic device, and a computer readable storage medium.

BACKGROUND OF THE INVENTION

With the development of technology and the change of market demand, thesize of display panels of electronic devices such as flat paneltelevisions has continuously increased, and people's demand for thequality of display have become higher and higher. However; after theimage is converted or transmitted, the quality may be reduced andblurring may occur. The edge of the image is enhanced, so that the imagequality is clear and sharp, and the visual experience is good, providinga higher-definition image for subsequent processing. The conventionaltechnology enhances the image noise while enhancing the edges of theimage. The fine texture of the image is also excessively enhanced andthe image becomes unnatural.

SUMMARY OF THE INVENTION

The main object of the present invention is to provide an image edgeprocessing method, an electronic device, and a computer readable storagemedium, aiming to solve the problem of how to balance the processing ofnoise and details while enhancing the edge of the image.

In order to realize the above purpose, the present invention provides animage edge processing method; comprising steps of: extracting abrightness component from an input image; calculating an edgeprobability value mp of each pixel in the image according to thebrightness component; calculating an enhancement coefficient A for eachpixel based on the edge probability value mp; performing a noisedetection according to the brightness component; and determining if eachpixel in the image is a noise point; when the pixel is not a noisepoint, performing a logarithmic processing to the pixel in order toobtain a data w; enhancing an edge of the image according to the A, thew and the brightness component in order to obtain an enhanced brightnesscomponent data; and after performing a brightness component synthesisaccording to the enhanced brightness component data, outputting anenhanced image.

Optionally, the method further comprises a step of: when a pixel is anoise point, performing a Gaussian filtering to the pixel in order toperform a noise reduction.

Optionally, after the step of extracting a brightness component from aninput image, the method comprise a step of: increasing the number ofbits of the brightness component in order to calculate an edgeprobability value mp of each pixel in the image according to thebrightness component after increasing the number of bits; after the stepof obtaining an enhanced brightness component data, the method furthercomprises a step of: converting the enhanced brightness component datato a low bit through dithering in order to perform the brightnesscomponent synthesis according to the brightness component data afterbeing converted.

Optionally; in the step of increasing the number of bits of thebrightness component, converting the number of bits of the brightnesscomponent from 8 bit to 10 bit or 12 bit; in the step of converting theenhanced brightness component data to a low bit, converting the numberof bits of the enhanced brightness component data from 10 bit or 12 bitto 8 bit.

Optionally, calculation formula of the edge probability value mp is:

f1=|y(j−1,i−1)+2*y(j−1,i)+y(j−1,i+1)−y(j+1,i−1)−2*y(j+1,i)−y(j+1,i+1)|;

f2=|y(j−1,i+1)+2*y(j,i+1)+y(j+1,i+1)−y(j−1,i−1)−2*y(j,i−1)−y(j+1,i−1)|;

f3=|Y(j,i−1)+2*y(j−1,i−1)+y(j−1,i)y(j+1,i)−2*y(j+1,i+1)−y(j,i+1)|;

f4=|y(j−1,i)+2*y(j−1,i+1)+y(j,i+1)−y(j,i−1)−2*y(j+1,i−1)−y(j+1,i)|;

mp (j,i)=max(f1f2f3f4); wherein, y(j, i) represents the brightnesscomponent of a pixel in the j-th row and i-th column, and mp(j, i)represents the edge probability value of a pixel (j, i).

Optionally, the noise detection comprises steps of: respectivelycalculating an absolute value of a brightness difference between atarget pixel and neighboring pixels of the target pixel; comparing eachcalculated absolute value with a preset threshold value. If thecalculated absolute value is smaller than the threshold value, acorresponding neighboring pixel is determined to be related to thetarget pixel, otherwise determined to be irrelevant; and counting thenumber of neighboring pixels related to the target pixel, if the numberis 0 or 1, the target pixel is determined to be a noise point.

Optionally, the step of enhancing an edge of the image according to theA, the w and the brightness component comprises steps of: for eachpixel, using a sharpening mask to perform a high-pass filtering to thedata w, and outputting an E value; multiplying the E value with theenhancement coefficient A to obtain an enhancement value; and adding theenhancement value to the brightness component to obtain an enhancedbrightness component data.

Optionally, the sharpening mask is a Laplacian operator or a Sobeloperator.

Besides, in order to achieve the above purpose, the present inventionalso provides an electronic device, comprising: a memory; a processor;and an image edge processing program stored in the memory and capable ofoperating in the processor; wherein when the image edge processingprogram is executed by the processor, steps of the image edge processingmethod as described above is realized.

Besides, in order to achieve the above purpose, the present inventionalso provides a computer readable storage medium, wherein the computerreadable storage medium is stored with an image edge processing program,when the image edge processing program is executed by a processor, stepsof the image edge processing method as claimed in claim 1 is realized.

The image edge processing method, the electronic device and the computerreadable storage medium provided by the invention can perform edgedetection and noise detection on an image, calculate an enhancementcoefficient after performing the edge detection, and determine a pixelof a noise point after performing the noise detection. When a pixel isdetermined to be noise point, a Gaussian filtering process is performed,when a pixel is determined to be not a noise point, the pixel is furtherenhanced. The outline of the output image is enhanced, the image becomesclear, and the quality of the image is improved.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an architecture diagram of an electronic device according to afirst embodiment of the present invention.

FIG. 2 is a flowchart of an image edge processing method according to asecond embodiment of the present invention.

FIG. 3 is a flowchart of an image edge processing method according to athird embodiment of the present invention.

FIG. 4 is a schematic diagram of a target pixel and neighboring pixelsin the present invention;

FIG. 5 is a schematic diagram of an enhancement coefficient calculationfunction curve in the present invention.

FIG. 6 is a schematic diagram of a logarithmic function curve in thepresent invention.

FIG. 7 is a schematic illustration of the process of un-sharp masking inthe present invention.

The realization, function characteristics and advantages of the presentinvention will be further described with reference to the accompanyingdrawings in conjunction with the embodiments.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

It should be understood that the specific embodiments described hereinare only used to explain the present invention and are not used to limitthe present invention.

First Embodiment

Referring to FIG. 1, the first embodiment of the present inventionprovides an electronic device 2. The electronic device 2 has an imagedisplay and image processing function and may be a flat paneltelevision, a smart TV, a tablet computer, a mobile phone, or the like.The electronic device 2 includes a memory 20, a processor 22, and animage edge processing program 28.

The memory 20 includes at least one type of readable storage medium forstoring an operating system and various types of application softwareinstalled on the electronic device 2, such as a program code of theimage edge processing program 28. In addition, the memory 20 may also beused to temporarily store various types of data that have been outputtedor will be outputted.

The processor 22 may be a Central Processing Unit (CPU), controller,microcontroller, microprocessor, or other data processing chip in someembodiments. The processor 22 is generally used to control the overalloperation of the electronic device 2. In this embodiment, the processor22 is configured to run program code or processing data stored in thememory 20, such as running the image edge processing program 28 and thelike.

When the image edge processing program 28 is executed by the processor22, the following steps are implemented:

(1) Extracting a brightness component from an input image.

(2) Increasing the number of bits of the brightness component.

(3) Calculating an edge probability value mp of each pixel in the imagebased on the brightness component after increasing the number of bits.

(4) Calculating an enhancement coefficient A for each pixel based on theedge probability value mp.

(5) Performing a noise detection based on the brightness component todetermine if each pixel in the image is a noise point.

(6) When a pixel is a noise point, performing a Gaussian filtering tothe pixel in order to perform a noise reduction.

(7) When a pixel is not a noise point, performing a logarithmicprocessing to the pixel to obtain a processed data w.

(8) Enhancing an edge of the image according to the A, w and brightnesscomponent to obtain an enhanced brightness component data.

(9) Converting the enhanced brightness component data to lower bits.

(10) After synthesizing the brightness component, outputting theenhanced image.

For detailed description of the above steps, please referring to asecond embodiment and a third embodiment described below, which will notbe described herein.

Those skilled in the art can understand that the structure shown in FIG.2 does not constitute the limitation of the electronic device 2, and theelectronic device 2 may also include other necessary components (such asa screen, etc.) or combining some components, or different components.

Second Embodiment

Referring to FIG. 2, the second embodiment of the present inventionprovides an image edge processing method applied to the electronicdevice 2. In this embodiment, according to different requirements, theexecution order of the steps in the flowchart shown in FIG. 2 may bechanged, and some steps may be omitted. The method includes thefollowing steps:

S100, extracting a brightness component from an input image.

Specifically, in general, each pixel in one image may include abrightness component, a chroma component, and the like. When performingan edge enhancement for the image, firstly, obtaining an input image,and then extracting the brightness component of the image. The colorspace of the image can adopt YUV, HSL, or HSV.

S102: calculating an edge probability value mp of each pixel in theimage according to the extracted brightness component.

Specifically, after the brightness component is extracted from theimage, the brightness component of each pixel requires respectivelyperforming an edge detection and a noise detection. After the edgedetection, an edge probability value mp will be outputted. The value ofmp can measure the probability that the pixel is at the edge.

In this embodiment, the edge detection refers to calculating the edgeprobability value mp of a target pixel according to the brightnesscomponent corresponding to each pixel (the target pixel) and itsneighboring pixels (the surrounding 8 pixels). As shown in FIG. 4, whichis a schematic diagram of the target pixel and neighboring pixels of thetarget pixel. Wherein, the pixel (j, i) is the target pixel, and theother pixels are the adjacent pixels of the pixel (j, i). This stepneeds to inspect each target pixel in the image and calculate thecorresponding edge probability value mp, respectively. For ease ofcalculation, the target pixel is calculated from a second row of theimage to a second last row, corresponding to each line, calculating fromthe second pixel to the second to last pixel. Calculation formula is asfollowing:

f1=|y(j−1,i−1)+2*y(j−1,i)+y(j−1,i+1)−y(j+1,i−1)−2*y(j+1,i)−y(j+1,i+1)|;

f2=|y(j−1,i+1)+2*y(j,i+1)+y(j+1,i+1)−y(j−1,i−1)−2*y(j,i−1)−y(j+1,i−1)|;

f3=|Y(j,i−1)+2*y(j−1,i−1)+y(j−1,i)y(j+1,i)−2*y(j+1,i+1)−y(j,i+1)|;

f4=|y(j−1,i)+2*y(j−1,i+1)+y(j,i+1)−y(j,i−1)−2*y(j+1,i−1)−y(j+1,i)|;

mp(j,i)=max(f1f2f3f4);

Wherein, y(j, i) represents the brightness component of a pixel in thej-th row and i-th column, and mp(j, i) represents the edge probabilityvalue of a pixel (j, i). The larger the edge probability value mp, themore likely the pixel is in the edge region.

S104: calculating an enhancement coefficient A for each pixel based onthe edge probability value mp.

Specifically, for the edge probability value mp of each pixel in theimage, after a weight value calculation, the enhancement coefficient Ais outputted. In this embodiment, the enhancement coefficient Acorresponding to each edge probability value mp can be searched in theenhancement coefficient calculation function curve. As shown in FIG. 5,which is a schematic diagram of the enhancement coefficient calculationfunction curve.

In FIG. 5, the horizontal axis indicates the edge probability value mp,and the vertical axis indicates the enhancement coefficient A. When theedge probability value mp is small, that is, the pixel is in a weak edgeregion, the corresponding enhancement coefficient A is smaller toprotect the fine texture; when the edge probability value mp is larger,and the corresponding enhancement coefficient A is also smaller in orderto prevent the enhanced data to overflow. After calculating the edgeprobability value mp of each pixel in the image, the enhancementcoefficient A corresponding to each edge probability value mp is foundfrom FIG. 5.

S106: performing a noise detection according to the brightnesscomponent, and determining if each pixel in the image is a noise point.When the pixel is a noise point, executing a step S108. When the pixelis not a noise point, executing a step S110.

Specifically, the process of noise detection includes;

(1) respectively calculating an absolute value of a brightnessdifference between a target pixel and neighboring pixels of the targetpixel. The calculation formula is as following:

f1=|y(j,i)−y(j,i−1|;

f2=|y(j,i)−y(j,i+1)|;

f3=|y(j,i)−y(j−1,i−1)|;

f4=|y(j,i)−y(j−1,i)|;

f5=|y(j,i)−y(j−1,i+1)|;

f6=|y(j,i)−y(j+1,i−1)|;

f7=|y(j,i)−y(1+1,i)|;

f8=|y(j,i)−y(1+1,i+1)|;

wherein y represents the brightness component corresponding to a pixel.

(2) Comparing each calculated absolute value with a preset thresholdvalue. If the calculated absolute value is smaller than the thresholdvalue, a corresponding neighboring pixel is determined to be related tothe target pixel, otherwise determined to be irrelevant.

(3) Counting the number of neighboring pixels related to the targetpixel. If the number is 0 or 1, the target pixel is determined to be anoise point. If the target pixel is a noise point, a noise reduction isrequired. On the contrary, the target pixel is required to be enhanced.

S108, performing a Gaussian filtering to the pixel.

Specifically, performing a Gaussian filtering to the pixel determined asa noise point in the image in order to reduce the noise.

S110: performing a logarithmic processing to the pixel in order toobtain a data w.

Specifically, for a pixel determined as a non-noise point in the image,the corresponding brightness component is logarithmically processed andthen output w. As shown in FIG. 6, which is a schematic diagram of alogarithmic function curve. In FIG. 6, the horizontal axis representsthe brightness component y, and the vertical axis represents theprocessed data w. The logarithmic processing mainly considers that thehuman visual characteristics include a logarithmic link. After thelogarithmic processing, the details of the dark regions of the image canbe sharpened more precisely, and the detail enhancement effect of thedark regions of the image is improved.

S112: enhancing an edge of the image according to the A, the w and thebrightness component.

Specifically, this step is called an un-sharpening masking process. Asshown in FIG. 7, which is a schematic diagram of an un-sharpeningmasking process. The un-sharpening masking process includes: for eachpixel, using a sharpening mask to perform a high-pass filtering to thedata w after the logarithmic processing, and outputting an E value. Thesharpening mask may be a Laplacian operator, a Sobel operator, or thelike. The E value is multiplied with the enhancement coefficient A toobtain an enhancement value, and the enhancement value is added to thebrightness component (input signal in FIG. 7) to obtain an enhancedbrightness component data (output signal in FIG. 7).

S114: After performing a brightness component synthesis, outputting anenhanced image.

Specifically, the enhanced brightness component data and a correspondingchroma component and other data are synthesized, that is, inverselytransformed into RGB data output through the corresponding color space.

The image edge processing method proposed in this embodiment can performthe edge detection and the noise detection in the image, and after theedge detection, the calculation of the enhancement coefficient isperformed. After the noise detection, the pixel determined as a noisepoint performs a Gaussian filtering. The pixels that are judged as beingnot noise points are further enhanced. The outline of the output imageis enhanced, the image becomes clear, and the quality of the image isimproved.

Third Embodiment

Referring to FIG. 3, the third embodiment of the present inventionproposes an image edge processing method. In the third embodiment, thesteps of the image edge processing method are similar to those of thesecond embodiment except that the method further includes steps S202 andS216.

The method includes the following steps:

S200, extracting a brightness component from an input image.

Specifically, in general, each pixel in one image may include abrightness component, a chrominance component, and the like. Whenperforming an edge enhancement for the image, firstly, obtaining aninput image, and then s extracting the brightness component of theimage. The color space that the image can adopt YUV, HSL, or HSV.

S202: increasing the number of bits of the brightness component.

Specifically, the number of bits of the brightness component isconverted from 8 bits to 10 bits or 12 bits or more.

S204: calculating an edge probability value mp of each pixel in theimage according to the brightness component after increasing the numberof bits.

Specifically, after the brightness component increases the number ofbits, the brightness component of each pixel requires respectivelyperforming an edge detection and a noise detection. After the edgedetection, an edge probability value mp will be outputted. The value ofmp can measure the probability that the pixel is at the edge.

In this embodiment, the edge detection refers to calculating the edgeprobability value mp of a target pixel according to the brightnesscomponent corresponding to each pixel (the target pixel) and itsneighboring pixels (the surrounding 8 pixels). As shown in FIG. 4, whichis a schematic diagram of the target pixel and neighboring pixels of thetarget pixel. This step needs to inspect each target pixel in the imageand calculate the corresponding edge probability value mp, respectively.For ease of calculation, the target pixel is calculated from a secondrow of the image to a second last row, corresponding to each line,calculating from the second pixel to the second to last pixel.Calculation formula is as following:

f1=|y(j−1,i−1)+2*y(j−1,i)+y(j−1,i+1)−y(j+1,i−1)−2*y(j+1,i)−y(j+1,i+1)|;

f2=|y(j−1,i+1)+2*y(j,i+1)+y(j+1,i+1)−y(j−1,i−1)−2*y(j,i−1)−y(j+1,i−1)|;

f3=|Y(j,i−1)+2*y(j−1,i−1)+y(j−1,i)y(j+1,i)−2*y(j+1,i+1)−y(j,i+1)|;

f4=|y(j−1,i)+2*y(j−1,i+1)+y(j,i+1)−y(j,i−1)−2*y(j+1,i−1)−y(j+1,i)|;

mp(j,i)=max(f1f2f3f4);

Wherein, y(j, i) represents the brightness component of ae pixel in thej-th row and i-th column, and mp(j, i) represents the edge probabilityvalue of a pixel (j, i). The larger the edge probability value mp, themore likely the pixel is in the edge region.

S206: calculating an enhancement coefficient A for each pixel based onthe edge probability value mp.

Specifically, for the edge probability value mp of each pixel in theimage, after a weight value calculation, the enhancement coefficient Ais outputted. In this embodiment, the enhancement coefficient Acorresponding to each edge probability value mp can be searched in theenhancement coefficient calculation function curve. As shown in FIG. 5,which is a schematic diagram of the enhancement coefficient calculationfunction curve.

In FIG. 5, the horizontal axis indicates the edge probability value mp,and the vertical axis indicates the enhancement coefficient A. When theedge probability value mp is small, that is, the pixel is in a weak edgeregion, the corresponding enhancement coefficient A is smaller toprotect the fine texture; when the edge probability value mp is larger,and the corresponding enhancement coefficient A is also smaller in orderto prevent the enhanced data to overflow. After calculating the edgeprobability value mp of each pixel in the image, the enhancementcoefficient A corresponding to each edge probability value mp is foundfrom FIG. 5.

S208: performing noise detection according to the brightness component,and determining if each pixel in the image is a noise point. When thepixel is a noise point, executing a step S210. When the pixel is not anoise point, executing a step S212.

Specifically, the process of noise detection includes:

(1) respectively calculating an absolute value of the brightnessdifference between a target pixel and neighboring pixels of the targetpixel. The calculation formula is as following:

f1=|y(j,i)−y(j,i−1|;

f2=|y(j,i)−y(j,i+1)|;

f3=|y(j,i)−y(j−1,i−1)|;

f4=|y(j,i)−y(j−1,i)|;

f5=|y(j,i)−y(j−1,i+1)|;

f6=|y(j,i)−y(j+1,i−1)|;

f7=|y(j,i)−y(1+1,i)|;

f8=|y(j,i)−y(1+1,i+1)|;

wherein y represents the brightness component corresponding to a pixel.

(2) Comparing each calculated absolute value with a preset thresholdvalue. If the calculated absolute value is smaller than the thresholdvalue, a corresponding neighboring pixel is determined to be related tothe target pixel, otherwise determined to be irrelevant.

(3) Counting the number of neighboring pixels related to the targetpixel. If the number is 0 or 1, the target pixel is determined to be anoise point. If the target pixel is a noise point, a noise reduction isrequired. On the contrary, the target pixel is required to be enhanced.

S210, performing a Gaussian filtering to the pixel.

Specifically, performing a Gaussian filtering to the pixel determined asa noise point in the image in order to reduce the noise.

S212: performing logarithmic processing to the pixel in order to obtaina processed data w.

Specifically, for a pixel determined as being not a noise point in theimage, the corresponding brightness component is logarithmicallyprocessed and then output w. As shown in FIG. 6, which is a schematicdiagram of a logarithmic function curve. In FIG. 6, the horizontal axisrepresents the brightness component y, and the vertical axis representsthe processed data w. The logarithmic processing mainly considers thatthe human visual characteristics include a logarithmic link. After thelogarithmic processing, the details of the dark regions of the image canbe sharpened more precisely, and the detail enhancement effect of thedark regions of the image is improved.

S214: enhancing an edge of the image according to the A, w and thebrightness component.

Specifically, this step is called an un-sharpening masking process. Asshown in FIG. 7, which is a schematic diagram of an un-sharpeningmasking process. The un-sharpening masking process includes: for eachpixel, using a sharpening mask to perform a high-pass filtering to thedata w after the logarithmic processing, and outputting an E value. Thesharpening mask may be a Laplacian operator, a Sobel operator, or thelike. The E value is multiplied with the enhancement coefficient A toobtain an enhancement value, and the enhancement value is added to thebrightness component (input signal in FIG. 7) to obtain an enhancedbrightness component data (output signal in FIG. 7).

S216: converting the enhanced brightness component data to a low bit.

Specifically, the enhanced brightness component data is dithered toshift from a high bit (10 bits or 12 bits) to a low bit (8 bits).

S218: performing a brightness component synthesis to output an enhancedimage.

Specifically, synthesizing the converted brightness component data and acorresponding chroma component, that is, inversely converted into RGBdata to output through a corresponding color space.

The image edge processing method proposed in this embodiment firstconverts the brightness component into a high bit to performcalculation, and then reduces the brightness component from a high bitto a low bit by a dithering display, thereby improving the operationaccuracy and making the image processing effect better.

Fourth Embodiment

Another embodiment of the present invention provides a computer-readablestorage medium having an image edge processing program stored therein.The image edge processing program may be executed by at least oneprocessor. The at least one processor is used to perform the steps ofthe image edge processing method as described above.

It should be noted that, in the present context, the terms “include”,“including” or any other variations thereof are intended to covernon-exclusive inclusions such that a process, method, article, orapparatus that includes a series of elements includes not only thoseelements but also includes other elements that are not explicitlylisted, or elements that are inherent to such processes, methods,articles, or devices. In the case of no more limitation, the elementdefined by the sentence “includes a . . . ” does not exclude thepresence of another identical element in the process, method, article,or apparatus that includes the element.

The sequence numbers of the foregoing embodiments of the presentinvention are merely for description and do not represent the advantagesand disadvantages of the embodiments.

Through the description of the above embodiments, those skilled in theart can clearly understand that the above embodiment method can beimplemented by means of software plus a necessary general hardwareplatform. Of course, the hardware can also be used, but in many cases,the former is better for implementation. Based on this understanding,the part of the technical solution of the present invention thatessentially or contributing to the prior art can be embodied in the formof a software product stored in a storage medium (such as a ROM/RAM, amagnetic disk, an optical disk), including several instructions used toenable a terminal (which may be a mobile phone, a computer, a server, anair conditioner, or a network device, etc.) to perform the methodsdescribed in the various embodiments of the present invention.

The embodiments of the present invention have been described above withreference to the accompanying drawings, but the present invention is notlimited to the above specific embodiments. The above specificembodiments are merely illustrative and not limitative, and thoseskilled in the art are in the light of the present invention, many formsmay be made without departing from the scope of the present inventionand the protection scope of the claims, and these are all included inthe protection scope of the present invention,

What is claimed is:
 1. An image edge processing method, comprising stepsof: extracting a brightness component from an input image; calculatingan edge probability value mp of each pixel in the image according to thebrightness component; calculating an enhancement coefficient A for eachpixel based on the edge probability value mp; performing a noisedetection according to the brightness component, and determining if eachpixel in the image is a noise point; when the pixel is not a noisepoint, performing a logarithmic processing to the pixel in order toobtain a data w; enhancing an edge of the image according to the A, thew and the brightness component in order to obtain an enhanced brightnesscomponent data; and after performing a brightness component synthesisaccording to the enhanced brightness component data, outputting anenhanced image.
 2. The image edge processing method according to claim1, wherein the method further comprises a step of: when a pixel is anoise point, performing a Gaussian filtering to the pixel in order toperform a noise reduction.
 3. The image edge processing method accordingto claim 2, wherein after the step of extracting a brightness componentfrom an input image, the method comprise a step of: increasing thenumber of bits of the brightness component in order to calculate an edgeprobability value mp of each pixel in the image according to thebrightness component after increasing the number of bits; and after thestep of obtaining an enhanced brightness component data, the methodfurther comprises a step of: converting the enhanced brightnesscomponent data to a low bit through dithering in order to perform thebrightness component synthesis according to the brightness componentdata after being converted.
 4. The image edge processing methodaccording to claim 3, wherein in the step of increasing the number ofbits of the brightness component, converting the number of bits of thebrightness component from 8 bit to 10 bit or 12 bit; in the step ofconverting the enhanced brightness component data to a low bit,converting the number of bits of the enhanced brightness component datafrom 10 bit or 12 bit to 8 bit.
 5. The image edge processing methodaccording to claim 1, wherein calculation formula of the edgeprobability value mp is:f1=|y(j−1,i−1)+2*y(j−1,i)+y(j−1,i+1)−y(j+1,i−1)−2*y(j+1,i)−y(j+1,i+1)|;f2=|y(j−1,i+1)+2*y(j,i+1)+y(j+1,i+1)−y(j−1,i−1)−2*y(j,i−1)−y(j+1,i−1)|;f3=|Y(j,i−1)+2*y(j−1,i−1)+y(j−1,i)y(j+1,i)−2*y(j+1,i+1)−y(j,i+1)|;f4=|y(j−1,i)+2*y(j−1,i+1)+y(j,i+1)−y(j,i−1)−2*y(j+1,i−1)−y(j+1,i)|;mp(j,i)=max(f1f2f3f4); wherein, y(j, i) represents the brightnesscomponent of a pixel in the j-h row and i-th column, and mp(j, i)represents the edge probability value of a pixel (j, i).
 6. The imageedge processing method according to claim 2, wherein calculation formulaof the edge probability value mp is:f1=|y(j−1,i−1)+2*y(j−1,i)+y(j−1,i+1)−y(j+1,i−1)−2*y(j+1,i)−y(j+1,i+1)|;f2=|y(j−1,i+1)+2*y(j,i+1)+y(j+1,i+1)−y(j−1,i−1)−2*y(j,i−1)−y(j+1,i−1)|;f3=|Y(j,i−1)+2*y(j−1,i−1)+y(j−1,i)y(j+1,i)−2*y(j+1,i+1)−y(j,i+1)|;f4=|y(j−1,i)+2*y(j−1,i+1)+y(j,i+1)−y(j,i−1)−2*y(j+1,i−1)−y(j+1,i)|;mp(j,i)=max(f1f2f3f4); wherein, y(j, i) represents the brightnesscomponent of a pixel in the j-th row and i-th column, and mp(j, i)represents the edge probability value of a pixel (j, i).
 7. The imageedge processing method according to claim 3, wherein calculation formulaof the edge probability value mp is:f1=|y(j−1,i−1)+2*y(j−1,i)+y(j−1,i+1)−y(j+1,i−1)−2*y(j+1,i)−y(j+1,i+1)|;f2=|y(j−1,i+1)+2*y(j,i+1)+y(j+1,i+1)−y(j−1,i−1)−2*y(j,i−1)−y(j+1,i−1)|;f3=|Y(j,i−1)+2*y(j−1,i−1)+y(j−1,i)y(j+1,i)−2*y(j+1,i+1)−y(j,i+1)|;f4=|y(j−1,i)+2*y(j−1,i+1)+y(j,i+1)−y(j,i−1)−2*y(j+1,i−1)−y(j+1,i)|;mp(j,i)=max(f1f2f3f4); wherein, y(j, i) represents the brightnesscomponent of a pixel in the j-h row and i-th column, and mp(j, i)represents the edge probability value of a pixel (j, i).
 8. The imageedge processing method according to claim 1, wherein the noise detectioncomprises steps of: respectively calculating an absolute value of abrightness difference between a target pixel and neighboring pixels ofthe target pixel; comparing each calculated absolute value with a presetthreshold value, if the calculated absolute value is smaller than thethreshold value; a corresponding neighboring pixel is determined to berelated to the target pixel, otherwise determined to be irrelevant; andcounting the number of neighboring pixels related to the target pixel,if the number is 0 or 1, the target pixel is determined to be a noisepoint.
 9. The image edge processing method according to claim 2; whereinthe noise detection comprises steps of: respectively calculating anabsolute value of a brightness difference between a target pixel andneighboring pixels of the target pixel; comparing each calculatedabsolute value with a preset threshold value, if the calculated absolutevalue is smaller than the threshold value, a corresponding neighboringpixel is determined to be related to the target pixel; otherwisedetermined to be irrelevant; and counting the number of neighboringpixels related to the target pixel, if the number is 0 or 1, the targetpixel is determined to be a noise point.
 10. The image edge processingmethod according to claim 3, wherein the noise detection comprises stepsof: respectively calculating an absolute value of a brightnessdifference between a target pixel and neighboring pixels of the targetpixel; comparing each calculated absolute value with a preset thresholdvalue, if the calculated absolute value is smaller than the thresholdvalue, a corresponding neighboring pixel is determined to be related tothe target pixel, otherwise determined to be irrelevant; and countingthe number of neighboring pixels related to the target pixel, if thenumber is 0 or 1, the target pixel is determined to be a noise point.11. The image edge processing method according to claim 1, wherein thestep of enhancing an edge of the image according to the A; the w and thebrightness component comprises steps of: for each pixel, using asharpening mask to perform a high-pass filtering to the data w, andoutputting an E value; multiplying the E value with the enhancementcoefficient A to obtain an enhancement value; and adding the enhancementvalue to the brightness component to obtain an enhanced brightnesscomponent data.
 12. The image edge processing method according to claim2, wherein the step of enhancing an edge of the image according to theA, the w and the brightness component comprises steps of: for eachpixel; using a sharpening mask to perform a high-pass filtering to thedata w, and outputting an E value; multiplying the E value with theenhancement coefficient A to obtain an enhancement value; and adding theenhancement value to the brightness component to obtain an enhancedbrightness component data.
 13. The image edge processing methodaccording to claim 3, wherein the step of enhancing an edge of the imageaccording to the A; the w and the brightness component comprises stepsof: for each pixel, using a sharpening mask to perform a high-passfiltering to the data w, and outputting an E value; multiplying the Evalue with the enhancement coefficient A to obtain an enhancement value;and adding the enhancement value to the brightness component to obtainan enhanced brightness component data.
 14. The image edge processingmethod according to claim 11, wherein the sharpening mask is a Laplacianoperator or a Sobel operator.
 15. The image edge processing methodaccording to claim 12, wherein the sharpening mask is a Laplacianoperator or a Sobel operator.
 16. The image edge processing methodaccording to claim 13, wherein the sharpening mask is a Laplacianoperator or a Sobel operator.
 17. An electronic device, comprising: amemory; a processor; and an image edge processing program stored in thememory and capable of operating in the processor; wherein when the imageedge processing program is executed by the processor, steps of the imageedge processing method as claimed in claim 1 is realized.
 18. A computerreadable storage medium, wherein the computer readable storage medium isstored with an image edge processing program, when the image edgeprocessing program is executed by a processor, steps of the image edgeprocessing method as claimed in claim 1 is realized.